In the fast-evolving world of Business Process Outsourcing (BPO), the rise of Artificial Intelligence (AI) has sparked a debate: Is AI a threat to the industry or an opportunity for transformation? With AI rapidly gaining traction, many BPO professionals wonder how these innovations will impact traditional outsourcing models.
AI in the BPO industry refers to integrating AI technologies, such as chatbots, voicebots, and robotic process automation (RPA), to streamline operations, enhance customer service, and reduce operational costs. AI enables BPOs to automate routine tasks, allowing human agents to focus on higher-value activities.
Replacing human agents
Capgemini’s recent $3.3 billion acquisition of business process outsourcing (BPO) company WNS had AI written all over it. AI is reinventing BPO in fundamental ways, and the two companies came together so that they could offer AI-powered business process transformation to enterprises, combining WNS’ understanding of business processes, and Capgemini’s AI expertise. There’s a big message in this for India, and for talent in the country. The BPO industry has traditionally been a big hirer- as many as 2 million people work in this industry today. But that people-intensive BPO model is flipping to a platform-led one.
The future, as many see it, is what is being called service-as-software, or agentic AI services, where AI-powered agents are deployed to perform tasks that were previously done by humans. These agents can be customised to specific workflows and adapt to changing needs, and customers would be charged based on the tasks or services completed, rather than a flat fee for software usage, or for the human agent. So, a company might pay based on the number of data points cleaned, issues resolved, or tasks completed. It will dramatically change the economics of BPO services. AI is already transforming how corporations tackle problems that were previously constrained by time, budget and compute resources.
Certain prototypes that used to take 8-12 weeks can now be produced in days or even hours. AI is resulting in threefold faster task completion and a 50 per cent drop in errors. The future won’t be built by scaling monolithic roles, but by designing intelligent networks of human and AI micro-specialists working in harmony. It is every company’s UnBPO philosophy moving beyond conventional BPO to create intelligent operating models.
AI tools being used by BPOs
Agent assist: These technologies help contact centre agents do their jobs more efficiently using screen pop-ups, sales suggestions and next best action recommendations which can create a more personalised customer experience. BPOs also have a lower average handle time than other industries due to better agent assist technologies.
Conversational AI: While conversational AI can serve as a chatbot channel within contact centres, customer service leaders can also use it internally to evaluate trends. For example, it could ask agents about their top drivers for success on a particular day. It also offers more access to data across the organisation, so agents can answer customers' questions more quickly.
Sentiment analysis: This type of analysis can fall under agent assist technologies because it can help agents improve customer experience. Sentiment analysis uses natural language processing to gauge customers' emotions through their written words or tone of voice. So, if customers get frustrated, AI can transfer them to a live agent, or another agent better equipped to handle their issues.
Text analytics: This analysis also falls under agent assist technologies, although it has less to do with customers' experiences. It's more suited to helping agents search through the organisation's data quickly to answer specific questions. Additionally, it could identify key terms or phrases in a transcript to analyse a specific issue, how the agent solved it and what could have been done better.
Self-service: Chatbots, FAQs and interactive voice response systems are the most common examples of AI self-service. These channels offer customers a route to independently solve their issues or answer their questions without talking to an agent. If contact centre leaders maintain these channels and ensure they offer correct, up-to-date information, it can free up agents to help customers with more pressing issues.
Bots: Nearly half of organisations employ chatbots, according to Metrigy research. Whether a business uses chatbots, voice bots or any other type, over half of them have staff dedicated to ensure they function properly, so the bots end up creating jobs rather than taking them away from agents.
Generative AI: Many BPOs also report using generative AI in their workflows for tasks like meeting transcripts, content creation for self-service channels or summaries for customer feedback. These trials put BPOs ahead of other industries in GenAI use as well.
People centres to intelligence hubs
Genpact, among the early movers in the business services space, has deployed over 1,000 AI models to solve specific business problems. Their AI Agent foundry builds and deploys “agentic AI”- autonomous systems that interpret context, resolve exceptions, and deliver faster, smarter decisions.
Take accounts payable. Say, a food processor places a purchase order (PO) for 1,000 kg of tomatoes. Due to moisture loss, spillage and transit damage, only 950 kg are received and recorded in the goods receipt note (GRN). But the supplier invoices for the full 1,000 kg. In a three-way match of invoice, GRN and PO, the accounts payable system detects a discrepancy. Traditionally, this gets escalated to a human reviewer, thus delaying processing by days, sometimes weeks. With an agentic AI solution for accounts payable, the AI recognises tomatoes as perishables, pulls historical data showing a typical five percent variance, and adjusts the threshold. The result? The invoice is processed automatically in seconds, not days.
Genpact has around 140,000 employees, 70 percent of them in India, and majority of them in the Delhi-NCR region which include Gurgaon and Noida. Business verticals that use unstructured data (raw text, images, audio, social media posts, etc) are seeing tremendous benefit from AI. For instance, EXL’s insurance LLM and negotiation agent automatically tags relevant data points, generates summaries from unstructured medical records and provides Q&A capabilities that assist claim adjusters in answering critical questions.
While some AI use cases- like information synthesis, customer experience transformation- are becoming table stakes, Firstsource is seeing emerging value for AI in areas like cyber defence, diagnostics and even molecule discovery. Vertex has embedded AI in nearly every facet of its operations. Their V Assist is an AI tool that provides real time communication cues. When a call begins, it’s initially handled by an AI bot, but the bot is equipped with embedded trigger points- specific cues programmed to detect moments that warrant escalation. Like when a customer sound dissatisfied. On identifying such cues, the system seamlessly hands over to a live agent. This is called a dynamic AI-HI (human intervention) hybrid model.
Must prepare for the future
Very soon, there will be no BPOs as they have been known in the past. Everything will be totally AI-driven. Routine tasks like research, knowledge extraction, knowledge extraction, analytics and report summarisation are already being fully automated. The days when good communication skills and basic domain training were enough to land a job at a BPO company are fading very fast. Today’s workforce needs expertise in statistical modelling, computer vision, agentic AI, data engineering, and more to stay relevant. Employees must know various platforms and tools like ChatGPT, Grok, Gemini, Jasper, Databricks (helps unify data analytics and AI) and so on, and build deep expertise in these.
Now, coding assistants like Cursor can allow enterprises to increase the output from their existing engineering workforce and dramatically improve the productivity of individual developers, which can empower enterprises to develop a lot more of these applications in-house. And with the rise of AI-powered web app builders, non-technical users will soon be able to build internal applications without needing to write code themselves. Both of these capabilities mean that in the future, there will be much less of a need to outsource application development to a BPO.
AI-native companies are already going after BPO spend, and many of them are growing at unprecedented rates. These companies know they need to build defensible products to stay ahead of both incumbent and startup competition, but the early evidence from customers- both the high demand and the evident customer love- mean that they’ll have a clear opportunity to build deeper product workflows and create enduring moats in the process.
Time to update academic curriculums
The New Education Policy (NEP) formulated by the Narendra Modi-led NDA government which is already undergoing nationwide implementation, lays focus on training the youth on basic AI skills and robotics at secondary-school level. NEP also focusses on grooming young school children with software coding skills at the primary level. Now, the government must update the CBSE curriculum accordingly, so that the next generation is fully trained with very advanced AI skillsets when they pass their higher secondary examinations and enter the college / university level higher education. These skills will be tremendously helpful in getting livelihood in the near future.
Future avenues for startups
Many startups are productising the BPO, but others are taking on the services themselves. Some businesses are taking a private equity-like roll-up strategy, where they buy assets and use AI to make them more efficient. Others are trying acquisition-as-distribution mechanisms to acquire customers that can prototype the software product they plan to make their long-term business. Yet other companies are building a new full-stack firm from the ground up to compete with existing BPOs on speed, cost and quality, embedding both AI and human judgment into their core DNA.
Tech-enabled businesses have historically been challenging because it is difficult to both build a first-class AI product and run a gritty operational business, but modern AI has made it easier to explore where a full-stack approach may be viable. These include:
Tackling industries that are reluctant to purchase software altogether: Some industries are simply late adopters and prefer buying outcomes instead of products; for these industries, it’s possible that delivering the outcome and leveraging AI internally is the optimal approach, as Foundation is trying to do for the insurance market and Long Lake is trying to do for HOAs. These companies will naturally face the traditional challenges of operating a tech-enabled service- namely, that it’s difficult to build first-class tech and run a first-class operational business- but if they can nail the motion, there are likely valuable businesses to be built.
Starting full-stack as a wedge into an eventual software play: Some companies plan to be a software-only business in the long-term, but start by making small service provider acquisitions to secure an initial customer base. They plan to use that base to observe the manual workflows and learn how to automate them with AI. While unconventional, this strategy could be an attractive way to get initial distribution and have a friendly prototyping environment for the software product, while still reaping the benefits of being a software business over the long run
Enabling the creation of frontier AI itself: Frontier AI is a category where human involvement is intrinsically important given the human judgment required to develop and train models. Scale- last reported to have a $13.8 billion valuation and one of the fastest-growing private companies today- in many ways started as the infrastructure for these novel AI needs. Its first major product was high-quality data for autonomous vehicle companies like Waymo, which required perfectly-annotated LiDAR and other sensor data to improve their self-driving capabilities. Scale sold outcomes (i.e., data that improved customer models) while building cutting-edge internal tools to do so efficiently and at high quality. With the advent of generative AI, they have since expanded to providing reinforcement learning with human feedback (RLHF) data- a critical component to training the most advanced foundation models- to the AI labs. In both of these cases, BPOs didn’t have existing offerings, failed to recognise the need early on, and lacked the technical underpinnings required to serve these needs, which only continued to grow in complexity. This left Scale well-positioned to capture the opportunity and obviated the need for a traditional outsourcing firm altogether. This path is rare since there are only so many frontier capabilities to serve (with Scale poised to capture many of them)- but it is an enormously valuable one if done correctly and at the right moment in time.
In the not-too-distant future, massive new companies and startups might be created that will completely subsume the work that traditional BPOs currently do.
By AMARTYA SINHA
(The content of this article reflects the views of writers and contributors, not necessarily those of the publisher and editor. All disputes are subject to the exclusive jurisdiction of competent courts and forums in Delhi/New Delhi only)
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